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A Framework for Integrated Software Quality Prediction Using Bayesian Nets

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Computational Science and Its Applications - ICCSA 2011 (ICCSA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6786))

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Abstract

The aim of this study is to develop a framework for integrated software quality prediction. This integration is reflected by a range of quality attributes incorporated in the model as well as relationships between these attributes. The model is formulated as a Bayesian net, a technique that has already been used in various software engineering studies. The framework enables to incorporate expert knowledge about the domain as well as related empirical data and encode them in the Bayesian net model. Such model may be used in decision support for software analysts and managers.

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Author information

Authors and Affiliations

  1. Department of Information Systems Engineering, University of Szczecin, ul. Mickiewicza 64, 71-101, Szczecin, Poland

    Łukasz Radliński

Authors
  1. Łukasz Radliński
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Editor information

Editors and Affiliations

  1. L.I.S.U.T. - D.A.P.I.T., Basilicata University Potenza, 10, Viale dell’Ateneo Lucano, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  3. Department of Applied Mathematics and Computational Sciences, University of Cantabria, Avda. de los Castros, s/n, C.P. 39005, Santander, Spain

    Andrés Iglesias

  4. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  5. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, Higashi-ku, 813-8503, Fukuoka, Japan

    Bernady O. Apduhan

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Radliński, Ł. (2011). A Framework for Integrated Software Quality Prediction Using Bayesian Nets. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6786. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21934-4_26

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  • DOI: https://doi.org/10.1007/978-3-642-21934-4_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21933-7

  • Online ISBN: 978-3-642-21934-4

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